Python longley.load_pandas函数代码示例

def test_formula_predict():
    from numpy import log
    formula = """TOTEMP ~ log(GNPDEFL) + log(GNP) + UNEMP + ARMED +
                    POP + YEAR"""
    data = load_pandas()
    dta = load_pandas().data
    results = ols(formula, dta).fit()
    npt.assert_equal(results.fittedvalues, results.predict(data.exog))

def test_formula_predict():
    # `log` is needed in the namespace for patsy to find
    from numpy import log  # noqa:F401
    formula = """TOTEMP ~ log(GNPDEFL) + log(GNP) + UNEMP + ARMED +
                    POP + YEAR"""
    data = load_pandas()
    dta = load_pandas().data
    results = ols(formula, dta).fit()
    npt.assert_almost_equal(results.fittedvalues.values,
                            results.predict(data.exog), 8)

def test_pandas_const_df_prepend():
    dta = longley.load_pandas().exog
    # regression test for #1025
    dta["UNEMP"] /= dta["UNEMP"].std()
    dta = tools.add_constant(dta, prepend=True)
    assert_string_equal("const", dta.columns[0])
    assert_equal(dta.var(0)[0], 0)

def test_summary_as_latex():
    # GH#734
    import re
    dta = longley.load_pandas()
    X = dta.exog
    X["constant"] = 1
    y = dta.endog
    res = OLS(y, X).fit()
    with pytest.warns(UserWarning):
        table = res.summary().as_latex()
    # replace the date and time
    table = re.sub("(?<=\n\\\\textbf\\{Date:\\}             &).+?&",
                   " Sun, 07 Apr 2013 &", table)
    table = re.sub("(?<=\n\\\\textbf\\{Time:\\}             &).+?&",
                   "     13:46:07     &", table)

    expected = """\\begin{center}
\\begin{tabular}{lclc}
\\toprule
\\textbf{Dep. Variable:}    &      TOTEMP      & \\textbf{  R-squared:         } &     0.995   \\\\
\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.992   \\\\
\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     330.3   \\\\
\\textbf{Date:}             & Sun, 07 Apr 2013 & \\textbf{  Prob (F-statistic):} &  4.98e-10   \\\\
\\textbf{Time:}             &     13:46:07     & \\textbf{  Log-Likelihood:    } &   -109.62   \\\\
\\textbf{No. Observations:} &          16      & \\textbf{  AIC:               } &     233.2   \\\\
\\textbf{Df Residuals:}     &           9      & \\textbf{  BIC:               } &     238.6   \\\\
\\textbf{Df Model:}         &           6      & \\textbf{                     } &             \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lcccccc}
                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\
\\midrule
\\textbf{GNPDEFL}  &      15.0619  &       84.915     &     0.177  &         0.863        &     -177.029    &      207.153     \\\\
\\textbf{GNP}      &      -0.0358  &        0.033     &    -1.070  &         0.313        &       -0.112    &        0.040     \\\\
\\textbf{UNEMP}    &      -2.0202  &        0.488     &    -4.136  &         0.003        &       -3.125    &       -0.915     \\\\
\\textbf{ARMED}    &      -1.0332  &        0.214     &    -4.822  &         0.001        &       -1.518    &       -0.549     \\\\
\\textbf{POP}      &      -0.0511  &        0.226     &    -0.226  &         0.826        &       -0.563    &        0.460     \\\\
\\textbf{YEAR}     &    1829.1515  &      455.478     &     4.016  &         0.003        &      798.788    &     2859.515     \\\\
\\textbf{constant} &   -3.482e+06  &      8.9e+05     &    -3.911  &         0.004        &     -5.5e+06    &    -1.47e+06     \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lclc}
\\textbf{Omnibus:}       &  0.749 & \\textbf{  Durbin-Watson:     } &    2.559  \\\\
\\textbf{Prob(Omnibus):} &  0.688 & \\textbf{  Jarque-Bera (JB):  } &    0.684  \\\\
\\textbf{Skew:}          &  0.420 & \\textbf{  Prob(JB):          } &    0.710  \\\\
\\textbf{Kurtosis:}      &  2.434 & \\textbf{  Cond. No.          } & 4.86e+09  \\\\
\\bottomrule
\\end{tabular}
%\\caption{OLS Regression Results}
\\end{center}

Warnings: \\newline
 [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline
 [2] The condition number is large, 4.86e+09. This might indicate that there are \\newline
 strong multicollinearity or other numerical problems."""
    assert_equal(table, expected)

def test_tests():
    formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
    dta = load_pandas().data
    results = ols(formula, dta).fit()
    test_formula = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
    LC = make_hypotheses_matrices(results, test_formula)
    R = LC.coefs
    Q = LC.constants
    npt.assert_almost_equal(R, [[0, 1, -1, 0, 0, 0, 0],
                               [0, 0 , 0, 1, 0, 0, 0],
                               [0, 0, 0, 0, 0, 0, 1./1829]], 8)
    npt.assert_array_equal(Q, [[0],[2],[1]])

def test_summary():
    # test 734
    import re
    dta = longley.load_pandas()
    X = dta.exog
    X["constant"] = 1
    y = dta.endog
    with warnings.catch_warnings(record=True):
        res = OLS(y, X).fit()
        table = res.summary().as_latex()
    # replace the date and time
    table = re.sub("(?<=\n\\\\textbf\{Date:\}             &).+?&",
                   " Sun, 07 Apr 2013 &", table)
    table = re.sub("(?<=\n\\\\textbf\{Time:\}             &).+?&",
                   "     13:46:07     &", table)

    expected = """\\begin{center}
\\begin{tabular}{lclc}
\\toprule
\\textbf{Dep. Variable:}    &      TOTEMP      & \\textbf{  R-squared:         } &     0.995   \\\\
\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.992   \\\\
\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     330.3   \\\\
\\textbf{Date:}             & Sun, 07 Apr 2013 & \\textbf{  Prob (F-statistic):} &  4.98e-10   \\\\
\\textbf{Time:}             &     13:46:07     & \\textbf{  Log-Likelihood:    } &   -109.62   \\\\
\\textbf{No. Observations:} &          16      & \\textbf{  AIC:               } &     233.2   \\\\
\\textbf{Df Residuals:}     &           9      & \\textbf{  BIC:               } &     238.6   \\\\
\\textbf{Df Model:}         &           6      & \\textbf{                     } &             \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lccccc}
                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$>$$|$t$|$} & \\textbf{[95.0\\% Conf. Int.]}  \\\\
\\midrule
\\textbf{GNPDEFL}  &      15.0619  &       84.915     &     0.177  &         0.863        &      -177.029   207.153       \\\\
\\textbf{GNP}      &      -0.0358  &        0.033     &    -1.070  &         0.313        &        -0.112     0.040       \\\\
\\textbf{UNEMP}    &      -2.0202  &        0.488     &    -4.136  &         0.003        &        -3.125    -0.915       \\\\
\\textbf{ARMED}    &      -1.0332  &        0.214     &    -4.822  &         0.001        &        -1.518    -0.549       \\\\
\\textbf{POP}      &      -0.0511  &        0.226     &    -0.226  &         0.826        &        -0.563     0.460       \\\\
\\textbf{YEAR}     &    1829.1515  &      455.478     &     4.016  &         0.003        &       798.788  2859.515       \\\\
\\textbf{constant} &   -3.482e+06  &      8.9e+05     &    -3.911  &         0.004        &      -5.5e+06 -1.47e+06       \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lclc}
\\textbf{Omnibus:}       &  0.749 & \\textbf{  Durbin-Watson:     } &    2.559  \\\\
\\textbf{Prob(Omnibus):} &  0.688 & \\textbf{  Jarque-Bera (JB):  } &    0.684  \\\\
\\textbf{Skew:}          &  0.420 & \\textbf{  Prob(JB):          } &    0.710  \\\\
\\textbf{Kurtosis:}      &  2.434 & \\textbf{  Cond. No.          } & 4.86e+09  \\\\
\\bottomrule
\\end{tabular}
%\\caption{OLS Regression Results}
\\end{center}"""
    assert_equal(table, expected)

 def setupClass(cls):
     data = dict((k, v.tolist()) for k, v in load_pandas().data.iteritems())
     cls.model = ols(longley_formula, data)
     super(TestFormulaDict, cls).setupClass()

res3 = sm.OLS(y, X).fit()


print(res3.f_test(R))


print(res3.f_test("x2 = x3 = 0"))


# ### Multicollinearity
# 
# The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification. 

from statsmodels.datasets.longley import load_pandas
y = load_pandas().endog
X = load_pandas().exog
X = sm.add_constant(X)


# Fit and summary:

ols_model = sm.OLS(y, X)
ols_results = ols_model.fit()
print(ols_results.summary())


# #### Condition number
# 
# One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length: 

def test_pandas_const_df():
    dta = longley.load_pandas().exog
    dta = tools.add_constant(dta, prepend=False)
    assert_string_equal("const", dta.columns[-1])
    assert_equal(dta.var(0)[-1], 0)

def test_pandas_const_series_prepend():
    dta = longley.load_pandas()
    series = dta.exog["GNP"]
    series = tools.add_constant(series, prepend=True)
    assert_string_equal("const", series.columns[0])
    assert_equal(series.var(0)[0], 0)

def test_pandas_const_series():
    dta = longley.load_pandas()
    series = dta.exog['GNP']
    series = tools.add_constant(series, prepend=False)
    assert_string_equal('const', series.columns[1])
    assert_equal(series.var(0)[1], 0)

def test_pandas_const_df_prepend():
    dta = longley.load_pandas().exog
    dta = tools.add_constant(dta, prepend=True)
    assert_string_equal('const', dta.columns[0])
    assert_equal(dta.var(0)[0], 0)